Method of analyzing sleep disorders

ABSTRACT

The invention provides a method of detecting, analyzing sleep disorders. The method includes the step of monitoring sound produced by a sleeping subject through a sensor proximate the sleeping subject and continuously recording the monitored sound. The method further includes identifying snoring within the recorded sound and analyzing the identified snoring to localize upper airway structural sources of snoring.

This is a continuation of application Ser. No. 08/231,025, filed Apr.21, 1994 now U.S. Pat. No. 5,671,733.

This application includes a microfiche appendix containing 21 frames.

The present invention relates to sleep disorders and in particular tomethods and apparatus for analyzing snoring and apnea.

BACKGROUND

An awareness of the risks of sleep disorders in recent years hasprompted a number of discoveries associated with sleep apnea andshoring. Sleep apnea is a known factor associated with heart problems.

Sleep apnea is generally regarded as an interruption in the breathingpattern of a sleeping subject. Interruptions of a breathing pattern maybe spontaneous or may result from a breathing obstruction such as asleeping subject's tongue blocking the airway or from partial orcomplete upper airway occlusion where the upper airway collapses,particularly under the reduced pressure generated by inhalation.Obstructive sleep apnea may result in lowered arterial blood oxygenlevels and poor quality of sleep.

It is estimated that there are more than 40 million chronic snorers inthe United States. Snoring is often a factor associated with sleepapnea. In addition to heart problems, sleeping disorders degrade thequality of rest for a person with the sleeping disorder as well as otherpeople, such as a spouse, sharing the sleeping quarters.

Prior art efforts to provide data relative to sleep disorders haveincluded the Sleep Apnea Monitor of U.S. Pat. No. 4,802,485. U.S. Pat.No. 4,804,485 provides a method of monitoring for sleep apnea thatincludes a number of sensors (blood-oxygen sensor, snoring sensor andhead position sensor) mounted to headgear of a monitored subject. Thesensors are, in turn, interconnected with a data logger for recordingand subsequent analysis by a doctor or technician.

Other patents, such as U.S. Pat. No. 4,982,738, have included additionalsensors for recording the time intervals between snoring events. Suchadvances have improved the content of the data recorded for lateranalysis by trained personnel.

Another advance, such as U.S. Pat. No. 5,275,159, have used a computerin conjunction with a data logger to improve the presentation ofrecorded data. The data logged under the invention of U.S. Pat. No.5,274,159 could be presented under any of three possible formats: (1) asa graph of sensor value versus time; (2) as histograms and tables; and(3) as episodes per hour of a selected parameter.

While the prior art has offered a number of improvements in thetechnology associated with presenting recorded data, the final diagnosisof the source of the sleep disorder still lies with the attendingphysician.

The most common surgical procedure used by physicians for correctingsleep disorders such as apnea or snoring is uvulopalatopharnygoplasty("UPPP"). Other procedures often used include adenoidectomy,tonsillectomy, septoplasty, turbinectomy, and polypectomy. In some casesphysicians even perform surgery of the hypopharynx and tongue.

In the case of snoring, if the generation site of the snoring is belowthe plane of the uvula, then surgery becomes very complicated and,often, impractical. Also, although there is no definitive method foridentifying sources of snoring, statistics show that UPPP reduces apnea50% of the time and snoring 75-80% of the time. Because of theimportance of the proper diagnosis of sleep disorders, a need exists fora simple and convenient method of determining the sources and types ofsleep disorders that is not completely dependent upon the judgment andexperience of an attending physician. It would be further advantageousto be able to easily identify the source of snoring to permit evaluationof the probability of success of the various surgical options.

SUMMARY OF THE INVENTION

In summary, the invention provides a method of analyzing sleepdisorders. The method includes the step of monitoring sound produced bya sleeping subject through a sensor proximate the sleeping subject andcontinuously recording the monitored sound. The method further includesidentifying snoring within the recorded sound and analyzing theidentified snoring to locate upper airway structural sources of snoring.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating a specific embodiment of a datarecording apparatus in accordance with the invention.

FIG. 2 is a perspective view of a specific embodiment of an acousticpick-up device on the head of a subject in accordance with theinvention.

FIG. 3 is a block diagram of a specific embodiment of an apparatus foranalyzing data from a subject in accordance with the invention.

FIG. 4 is a flow chart of data analysis in accordance with theinvention.

FIGS. 5A and 5B is a flow chart for breath detection in accordance withthe invention.

FIGS. 6A-6F is a flow chart of apnea/snoring analysis in accordance withthe invention.

BRIEF DESCRIPTION OF APPENDIX 1

Appendix 1 (see microfiche appendix) lists the source code routines ofthe present invention. Table 1 provides key routines corresponding tothe processes depicted in FIGS. 5 and 6.

BRIEF DESCRIPTION OF APPENDIX 2

Appendix 2 (see microfiche appendix) contains a list of the text withinFIGS. 5 and 6 along with references.

BRIEF DESCRIPTION OF THE PREFERRED EMBODIMENT

The solution to the problem of analyzing sleep disorders lies,conceptually, in data logging appropriate physiological characteristics(such as sound, body position, blood oxygen levels, et cetera) of asleeping subject and using the logged data within a formalistic processto identify types and sources of sleep disorders. It has been determinedthat frequency content of snoring of most subjects will differ basedupon the structural source within the upper airway causing the snoring.

The frequency of snoring caused by the palate and uvula will differ, forexample, from the frequency content of snoring caused by sites lower inthe throat. Further determination that a sleeping subject snores throughthe subject's nose instead of the subject's mouth (or visa versa) may beused to eliminate certain structures as possible sources of the snoring.

The technique of analyzing sleep disorders provided under the inventionlocalizes the source of the sleep disorder based upon informationcontained within the logged data. Under the invention, apnea andhypopnea are identified by comparison of characteristic parameterswithin the logged data with characteristic threshold values. Snoring, onthe other hand, is analyzed by relating the frequency content of thesnoring with upper airway structural sources. To identify structuralsources, an analysis is performed within the frequency domain on anaudio portion of the logged data to identify and measure fundamentalfrequencies and harmonics associated with the upper airway structures ofthe subject's breathing passages. It has been determined empiricallythat snoring sounds associated with structures such as the soft palateand uvula have fundamental frequencies that are typically in the rangeof from 20-300 Hertz while snoring originating from other structures(other than the throat) lie at higher frequencies. Throat snoring, whilehaving frequencies in the range of from 20-300 Hz has a diffusefrequency content.

It has also been determined that snoring sounds emanating from nasalpassages have higher fundamental frequencies than sounds originatingfrom the mouth. Snoring sounds originating from the mouth, while lowerin fundamental frequencies, are also somewhat more diffuse in frequencycontent.

The tonsils of some subjects have been determined to affect thefundamental frequency of snoring. Where the tonsils interfere withmovement of the tongue and uvula, a fundamental frequency of greaterthan 130 Hz would be expected.

FIG. 4 is a flow chart describing the method used under an embodiment ofthe invention. Reference will be made to FIG. 4 as appropriate toprovide an understanding of the invention. It is to be understood thatunder the invention some steps of the invention may be performedmanually or the entire process of FIG. 4 may be executed automaticallyunder the control of a general purpose computer.

To facilitate analysis of respiratory and snoring sounds, the datalogged audio information is limited to a frequency range of from 0-1250Hertz. Local maxima are then identified within the audio information aswell as gaps in respiratory sounds exceeding 10 seconds. Where gaps inexcess of 10 seconds are detected, an output is provided indicatingpossible apnea.

A frequency domain conversion i.e., a fast fourier transform ("FFT")! isperformed on the audio information. The fast fourier transformed audioinformation is then examined at the temporal locations of the previouslyidentified local maxima. Where the results of the FFT at the sites ofthe local maxima indicate that the local maxima is predominantly made upof a fundamental frequency in the proper frequency range and multiplesof the fundamental, a determination is made that the sleep disorder is asoft palate event associated with the palate and uvula (velum snore).Identification of the sleep disorder to be a soft palate event providesthe beneficial effect of indicating, in advance of surgery, that UPPPwill, more likely than not, be successful.

Data logging under the invention may be accomplished by any of a numberof prior art methods. Heart rate, respiratory and snoring sounds, oxygensaturation of the blood and body position information, for instance, maybe recorded as in U.S. Pat. No. 5,275,159, the disclosure of which ishereby incorporated by reference. Respiratory exertion may also berecorded from a strain gauge encircling the chest or abdomen of asleeping subject. Under a preferred embodiment of the invention, adigital audio tape (DAT) recorder, Walkman AVLS, model TCD-D7 made bythe Sony Corporation is used for logging audible and physiologicalevents.

Turning now to FIG. 1, a two-channel DAT recorder 15 is shown inconjunction with a number of sensing devices 10, 17, 18 and 19. The SaO₂sensor 17 is a conventional blood oxygen sensor that may be used todetermine blood oxygen saturation based upon spectral absorption of abeam of light passing through a body appendage such as a finger or anearlobe. The body position sensor 18 may be a plastic tetrahedron with ametal ball inside and wires located at each apex of the tetrahedron andwherein body position is determined by the metal ball making contactwith the wires at a particular apex of the tetrahedron. The optionalheart rate detector 19 may be an acoustic detector. Other suitablealternative sensors for oxygen, position and heart rate are known in theart.

The sensor 10 in one embodiment is an acoustical pick-up device forrespiration and snoring sounds originating from the nose or mouth of asleeping subject. The mode of use of this sensor 10 may be more fullyappreciated by reference to FIG. 2 where a strap 14 (not shown in FIG.2) of the sensor 10 is placed around the head of the subject, therebyholding an acoustic pick-up tube 13 in proximate relation to the noseand mouth of the subject. Two short tubes 21 positioned at the nostrilsof the subject (FIG. 2) conduct sounds from the nose of the sleepingsubject through the pick-up tube 13 to a microphone 12. An optionalsecond microphone 11 may detect respiratory and snoring sounds from themouth of the subject. Alternatively, mouth and nasal sounds may bedetected by microphone 12 by using a short tube in place of themicrophone 11 to conduct sound from the mouth to the pick-up tube 13.

Optionally, a single microphone may be placed approximately 40centimeters from the nose and mouth of a sleeping subject (or a singlecontact microphone may be placed on the throat of the sleeping subject)for the acoustical pick-up of respiratory and snoring sounds. The use ofthe sensor 10 or optional microphones has been determined to be moreeffective in detecting the sounds of snoring and/or airflow because ofthe proximity of the sound source to the sensor 10. Such detectors havebeen found useful under the invention in collecting apnea information aswell as in providing improved signal-to-noise ratios.

Data encoders 16 and 20 are used to encode data for recording on each ofthe two channels of the DAT recorder 15. Data encoder 20 frequencylimits microphones 11 and 12 to a bandwidth from 20-1250 Hertz and(where signals from two microphones are to be recorded on a singlechannel) may frequency shift (e.g., by modulating onto a 5 Khz carrier)the output of one of microphones 11 or 12 to a non-conflicting locationwithin the 0-20 kilo-Hertz bandwidth DAT channel such that audioinformation from microphones 11 and 12 may be recorded and, later,separately recovered without loss of information. Other suitableencoding schemes are well known in the art.

Data encoder 16 may function similarly to data encoder to encode datafrom the sensors 17, 18, 19, or use some other, simpler encodingprocess. Since the output of sensors 17-19 is analog and very lowfrequency, the information from the three sensors 17-19 may be eitherfrequency shifted for storage on the DAT channel, as with encoder 20,encoded under a time division multiplex (TDM) format, or otherwiseencoded to permit multiple sensor outputs to be recorded on a singlechannel. Alternatively, separate recording channels can be used torecord each sensor output.

To maximize recording efficiency, the data encoder 20 may buffer 2-3seconds of data and may compare a sound level at an input to the bufferwith a number of threshold levels, including a first and second soundamplitude threshold values. (The first threshold may be referred to asan apnea threshold and the second threshold referred to as a snoringthreshold). When the sound level rises above the first threshold,recording may be discontinued after some time period (e.g., 30 seconds).When the sound level falls below the first threshold (indicatingbreathing has stopped) recording may be restarted, resulting in 2-3seconds of buffered data (occurring before the threshold transition)being recorded first. If the sound level stays below the first amplitudethreshold for a third time period (e.g., 90 seconds) (indicating thatthe subject has awakened or the microphone has fallen off) the recordermay be again stopped.

Where the sound level exceeds the snoring threshold, recording isstarted after a fourth time period (e.g., 1 second) with, again, thebuffered data recorded first. When the sound level falls below thesnoring threshold, the recorder continues for a period allowing thesleeping subject to draw another breath. If the sound level does notagain exceed the snoring threshold for a fifth time period (e.g., 45seconds) recording may be again stopped.

After recording of physiological events for an appropriate period ofsleep (e.g., 4-8 hours), the DAT recorder 15 is disconnected from thesleep subject and the results analyzed. To facilitate recovery of therecorded data, the DAT recorder 15 is interconnected (FIG. 3) with ageneral purpose computer (processor 30) through analog to digitalconverters (A/D's) 28, 29. Decoding (and separation) of individualmicrophone 11, 12 outputs may be accomplished digitally within theprocessor 30, as is well known in the art, or within optional decoders26 or 27. Likewise, recovery of sensor 17-19 outputs may be accomplishedwithin the processor 30 or within the decoder 26 or 27.

Under one embodiment of the invention, recovery of data occurs at a veryrapid rate by replaying the recorded data at 2-4 times recording speedinto A/D's 28, 29 with frequency downconversion occurring within theprocessor 30. Alternatively, a number of A/D's are provided and datafrom many recorders 15 are recovered in parallel by the processor 30with the data from each recorder placed in a separate file for lateranalysis.

Upon transfer of the raw data into the processor 30, a first filecontaining audio information is created by the processor 30. The file isused by the processor to monitor the sound of the sleeping subject andidentify breathing and snoring events. To facilitate data analysis, theprocessor 30 creates a sound envelope of breathing activity detected byeach audio sensor.

To create a sound envelope, the processor 30 breaks the raw data intooverlapping blocks (e.g., 300 samples per block with 100 samples at afirst end overlapping a previous block and 100 samples at a second endoverlapping a subsequent block). An absolute value of the largest sampleof each block is stored in a reduced data file along with informationdetailing the location within the raw data of the largest sample of eachdata block.

Since the reduced data file contains a summary of the largest samplesover a number of data blocks, the contents of the reduced data file maybe displayed on a computer terminal as a sound envelope representationof the raw data file. Also, because of a 200:1 data reduction, severalminutes of raw data may be displayed on the terminal as a sound envelopethat is representative of the contents of the raw data file.

Under an embodiment of the invention drawn primarily to practicing theinvention under a manual mode, an operator (not shown) of the processor30 may use the sound envelope to identify likely episodes of apnea,hypopnea, or snoring. Using appropriate icons, the operator may scrollthe sound envelope or raw data displayed on the terminal forward orbackward to quickly identify profiles within the sound envelope or rawdata indicating such events. An icon such as a time bar may be used inconjunction with scroll icons to quickly move from one area of recordeddata to another. Split screen capability is also provided such thatsections of this sound envelope (or raw data) may be compared with othersections of data. The operator may identify such episodes to theprocessor 30 by clicking and dragging a computer mouse across such anepisode displayed on the monitor. Upon identifying an episode in such amanner, the processor 30 retrieves corresponding raw data andreconstructs the sound of the raw data through an audio speakerproximate the operator. Using such a method, an operator maydifferentiate between episodes of no breathing or inefficient breathing(apnea or hypopnea) and episodes of very quiet breathing. The operatormay also use such an approach to differentiate between snoring andcoughing or sneezing.

Alternatively, an operator may be trained to monitor the raw datathrough an audio speaker with the raw data played at 2-4 times therecorded speed. Upon identifying suspicious intervals, the operator mayreturn the speed of play to a normal rate to hear a normal reproductionof a suspicious event.

In the case of apnea or hypopnea, the operator may identify suchepisodes by listening 100 to the sound and noting a duration of such anepisode. Where episodes exceed some predetermined time length (e.g., 10seconds) 102, the operator may cause that part of the sound envelope tobe surrounded by a box (marker) and labeled with an appropriatecharacter (e.g., "A" for apnea or "H" for hypopnea). The processortabulates a total number of labeled boxes for the later generation of asummary.

In the case of snoring, the operator selects a section of data with anindication of snoring 103 and causes the processor 30 to do a frequencydomain conversion (e.g., a fast fourier transfer) 104 on the selecteddata. Fast fourier transformation may be accomplished by using theprogram provided on page 163 of The Fast Fourier Transform by E. OranBrigham (Prentice-Hall 1974)!. The processor 30, upon transforming thedata then displays the results for the benefit of the operator.

Fast fourier transformation (FFT) allows an operator to examine thefrequency components of selected snoring events. Since differentsections of the upper airway generate different signatures (frequencycomponents), the identification 105 of those frequency components in theselected data provides a means of diagnosing the source of the snore.The most significant contributors to snoring in the upper airway (andthe best candidate for UPPP) is the uvula and soft palate.

Snoring generated by the soft palate has a distinct pattern. Afundamental frequency of snoring generated by the soft palate istypically between 25-150 Hz and depends on the size of the uvula andsoft palate, whether the snoring takes place during the inhalation orexhalation and whether the snoring was nasal or oral, or both. Toevaluate fundamental and harmonic information within an episode (epoch),the operator characterizes the FFT data using a snore index 106 (e.g.,where the episode is primarily of a fundamental and harmonics, a snorescore of 1 is assigned and where fundamental or harmonics arenegligible, a snore score of 5 is assigned). A snore index can becalculated as an average of the snore scores. The processor 30 tabulatesthe snore index and location as each episode is evaluated for purposesof the later generation of a summary report.

The fundamental frequency of snoring often changes during a snoringepoch (e.g., during inhalation or exhalation) because of changes in thesize of the airway, air flow rate, etc. As a consequence, harmonics ofthe fundamental frequency are also present and changing. To accommodateand identify fundamental frequencies in a changing physical environment,the operator may be forced to reduce the affect of frequency changes inthe search window. Narrowing the search window will often allow anepisode that may originally have been a snore score 5 to be rated at amuch lower snore score number. Narrowing the search window (reducing aterminal size of analogical data) offers such benefits by examining amuch shorter term period where any shift in the fundamental frequencywould presumably be much smaller. On the other hand, a narrow searchwindow is to be avoided wherever possible because the use of a broadsearch window provides better resolution which is important at lowfrequencies.

It has been determined that an excellent candidate for UPPP is a subjectwith an identifiable fundamental and harmonics in each snoring epoch andvery little energy in other frequencies (snore score=1). If thefundamental and harmonics are not easily identifiable, then the softpalate and uvula may not be a significant contribution to the snoring orthat there may be other important sources of the snoring. (UPPP in sucha case would be less successful in reducing snoring).

To simulate the effects of UPPP, the operator may eliminate 107 thefundamental frequency and harmonics within an episode, and simulate theeffect through an audio speaker. The operator may eliminate thefundamental frequency and harmonics by individually selecting eachfrequency on the display of the processor 30 and activating a delete orattenuation function. Simulation of the effect is accomplished byperforming an inverse fast fourier transform (IFFT) and routing theresult to an audio speaker. A simulation of the effect of UPPP may thenbe determined by comparing decibel levels 108 of the original snoresplayed back and the snores after deletion/reduction offundamental/harmonics. Where differences in decibel level exceed somethreshold value 109 (e.g., 17 db) a snore score of 1 would be indicated.A snore index of 1 is an indication of a soft palate event 111. Wherethe difference is some other threshold (e.g., less than 17 db), a highersnore index may be assigned indicating snoring of mixed origin 111.

Upon completion of analysis under the manual mode, a summary report maybe generated by the processor 30. The summary report may includetabulations of such events as total number of apnea/hypopnea and snoringevents detected within the data. Based upon the number of snore events alisting may be provided as to the number of snore events assigned toeach snore index. Based on the distribution of snores among the snoreindex values a projection as to the site of snoring generation may beprovided as part of this summary.

In another embodiment of this invention, sleep analysis (snoring, apnea,etc.), and report generation may be performed automatically by theprocessor 30. To analyze sleep disorders, the processor 30 identifiesbreathing events and searches for temporal gaps in such breathing eventsfor apnea and hypopnea. The temporal gap between may be analyzed forapnea and hypopnea. Also, when the period between inhalation andexhalation (or intervening non-breathing event) have been identified,the analysis of snoring may be limited to more relevant areas.

Turning now to the identification of breathing events, a block diagramof the process may be found in FIG. 5. Reference will be made asappropriate in the explanation of blocks of FIG. 5 to correspondinglocations source code in Appendix 1 (see microfiche appendix).References to Appendix 1 (see microfiche appendix) will be toalphanumeric characters (B1-B8) located in the left margins.

In the identification of breathing events, the data source is thereduced data file. After each sample is retrieved 200 from the reduceddata file, the sample is compared 201 (source code location B1) to abreathing threshold level (breath.thresh). If the data sample exceedsbreath.thresh, then a determination is made 202 of whether the datasample is the first of a breathing event (breath.beg=0) (source codelocation B2). If the sample is the first of a breathing event, then abreath counter is set to zero 203 (source code location B3). To measurethe duration of the breath. After the breath counter is set to zero 203,or if the sample wasn't the first in a breathing event, then the breathcounter is incremented 206 (source code location B4). A breath resetcounter is also reset. A comparison is then made as to whether thecurrent sample is the largest sample for that breathing event 208(source code location B5). If the current sample is the largest sample,then a previous maximum is replaced by the current sample.

Through the blocks described 200-208, the breathing event detector (FIG.5) measures the duration of the breathing event and locates a relativemaximum for that breathing event. At the end of the breathing event (orduring apnea) data samples no longer exceed the breath threshold and adifferent path is taken out of block 201.

After a breath is over (breath samples no longer exceed breath.thresh),the samples are each used to increment 204 (source code location B6) abreath reset counter. The contents of the breath reset counter are thencompared with a threshold 205 (source code location B7) to determine ifenough continuous samples below the breath threshold have been receivedto indicate that the breathing event is over. If so, the contents of thebreath counter are then compared with a threshold 207 (source codelocation B8) to determine if the number of continuous samples exceedingthe threshold were enough to consider the breathing event a full breath.

After the termination of breathing events, the breath threshold, anapnea threshold, and a hypopnea threshold are recalculated 211 (sourcecode location B10) based upon an average 210 (source code location B9)of the last 10 breath maximas. Afterwards, or if the breathing eventwere determined to be a breath, then an apnea counter (apnea.count) isset equal to a current value of non-breath samples (breath.reset) andthe processor 30 proceeds to look for apnea.

To this end and in a general sense, the processor 30 identifies gaps 100with very little or no respiratory sound. Gaps are identified bycomparing sound levels within a moving ten-second block of audioinformation with a threshold value. Where the sound threshold (apneathreshold) is not exceeded for ten continuous seconds 102 (and did notcontinue for more than 90 seconds), the processor 30 outputs indication101 that the subject may have sleep apnea. In addition, the processor 30may output an indication of blood oxygen level during the gap along withsleep position or may defer providing indication of sleep apnea unlessthe blood oxygen level falls below a threshold level.

During each breathing event (FIG. 5) whenever the breathing threshold(breath.thresh) is not exceeded for a number of samples exceeding abreath-reset threshold 205, the processor looks for apnea (FIG. 6). Theprocessor 30 looks 300 for apnea by reading another data sample 301(source code location AP1) and compares the data sample with an apneathreshold (apnea.thresh) 302 (source code location B1).

If the data sample does not exceed the apnea threshold, then adetermination is made as to whether the sample is the beginning of anapnea interval 303 (source code location AP2). If the sample is thebeginning of an apnea interval, an apnea counter (apnea.count) is set tozero 304 (source code AP3). Afterwards, or if the sample was not thebeginning of an apnea event, an apnea counter (apnea.count) isincremented 305 (source code AP4) and an apnea reset counter(apnea.reset) is reset. For as long as the apnea period continues, theprocessor 30 processes data samples through blocks 301-305, each timeincrementing the counter apnea.count.

At the end of an apnea period (the data sample exceeds apnea.thresh),the apnea reset counter is incremented 306 (source code AP5). Whenenough continuous samples exceeding the apnea threshold have beenprocessed, such that the incrementing apnea reset value 306 exceeds athreshold 307 (source code AP6) a determination is made 308 thatbreathing has again begun.

After breathing again starts, a determination is made as to whether theduration of the apnea interval exceeded a threshold value 310 (sourcecode AP7). If they were, the interval is an apnea event 311 (source codeA8). If not, the interval was not apnea 313 (source code A8) and setsbreathing parameters 312 (source code A8) to examine another breathingevent.

Following the analysis of apnea, the processor 30 then analyzes snoringevents. In a general sense and to be able to analyze snoring, theprocessor must be able to identify local maxima within the raw data.

To identify 103 local maxima, the processor examines a moving one-halfsecond block of audio information for the highest relative magnitudeaudio event. To insure that the maxima is a significant event, theprocessor compares the maxima with adjacent audio information within theone-half second block to ensure that the maxima is at least twice theaverage of the magnitude of adjacent information within the one-halfsecond block. In processing the raw data, the processor 30 creates asecond file detailing the location of the local maxima within the rawdata.

The raw data containing the local maxima is then fast fouriertransformed 104 and may be stored in a third file by the processor 30.The previously identified local maximas are examined. Where the fastfourier transformed local maximas are comprised primarily of afundamental frequency between 20-300 Hertz, and harmonics of thefundamental frequency, the processor 30 outputs 111 an indication thatthe snore or sound is substantially a soft palate event.

To facilitate the FFT in the illustrated embodiment, 1,024 data points(i.e., samples) centered around the local maxima are selected fortransformation. Following transformation, a fundamental frequency isidentified by finding a second set of maximas within the FFT data.Maximas within the FFT data are identified by calculating a smoothed FFTfor each FFT data point. The smoothed FFT (S_(i)) is calculated asfollows: ##EQU1## where "i" is the frequency, F_(i) is the FFT amplitudeat the "ith" frequency, M_(i) is the smoothing order (e.g., 4), and 20Hz<i< 300 Hz. To determine a maxima each S_(i) is compared with itsneighbors on each side. S_(i) is a maxima if S_(i) is greater than orequal to S_(j) for all i-m₂ <j<i +m₂ where m₂ is a range of evaluatedFFT data points (e.g., 10). An actual maxima is determined around eachfrequency of S_(j) (i_(sj)) for one data point is less than or equal toS_(j) is less then or equal to m₂ data points. An FFT point is a maxima(F_(imax)) if F_(imax) is greater than or equal to F_(ik) for any i_(k)where i_(sj) -m₂ <i_(k) <i_(sj) +m₂ .

Insignificant FFT maximas are eliminated by considering surrounding FFTdata points. If the magnitude of surrounding FFT points do not drop tosome proportional value (e.g., 1/3) within M₃ FFT points (e.g., 10) andstay below the proportional value for an additional M₄ FFT points (e.g.,40), then the FFT maxima is dropped from consideration. To state thepremise in another manner F_(imax) is a significant maxima if F_(imax)is >3F_(i) ; where i_(max) -M₃ -M₄ <i<i_(max) -M₃.

Once the insignificant FFT maximas have been eliminated, the remainingFFT maximas are ordered in terms of increasing frequency. An attempt ismade to group FFT maximas in terms of a fundamental frequency and itsharmonics. The first FFT maxima of the ordered group, at the lowestfundamental frequency i_(l), is used to identify harmonics through theuse of the equality i_(k) =i_(l) *l±δ where i_(k) is a harmonic ofi_(l), l is an integer greater than one, and δ is an allowed error(e.g., 1). Any FFT maximas at a frequency of i_(l) and i_(k) isconsidered part of a harmonic group which included the fundamentalfrequency and any harmonics.

The process can be repeated for the next fundamental frequency (FFTmaxima) of the remaining FFT maximas within the ordered group. The FFTmaxima at the next highest frequency i₂ (and multiples) become part of asecond harmonic group. The process may be repeated again and again untilmore FFT maximas have all been included in some harmonic group.

It has been noted in some cases that a fundamental frequency may not beincluded within the original group of FFT maximas. When this happens, itwill not be possible to group some FFT maximas of the ordered group ofFFT maximas within a harmonic group. One means of solving this problemis to divide the frequency of the lowest frequency FFT maxima of theremaining ordered group by 2 (or 3) and use the result as a fundamentalfrequency. Using such a procedure can make it possible to place more FFTmaximas within a harmonic group.

Once the FFT maximas have been placed into harmonic groups, the harmonicgroups having fundamental frequencies in the range of from 20-300 Hz areidentified 105. Since it has been determined that snoring havingfundamental frequencies in the range of from 20-300 Hz is an eventprimarily associated with the structures of the soft palate and uvulaand since UPPP can be effective in reducing snoring produced by theuvula, the determination of the contribution of sound produced by theuvula provides an important benefit.

To evaluate the sound contribution of the soft palate and uvula, theidentified 105 harmonic groups having fundamental frequencies in therange of from 20-300 Hz are subtracted 107 from the original sounds ofsnoring and the decibel reduction in volume of the FFT residual maximaevaluated 108. If it is noted that the reduction exceeds some threshold(e.g., 17 db) 109, then the site of snoring generation is the velum andthe subject may be a good candidate for UPPP 111.

To facilitate a comparison of sound levels, the FFT maximas (datapoints) of the identified harmonic group are subtracted from theoriginal fast fourier transformed data and the result is optionallystored in a fourth file. To obtain a decibel comparison between the FFTpoints in the third and fourth files, a weighing factor must beassociated with each of the FFT values within each data file based uponfrequency. Through the use of the weighing, the loudness of the thirdand fourth files can be compared. Given an FFT value of F_(i) (0<i<N-1and N is the order of the FFT volume), the effective loudness L(f) in Dbcan be determined by evaluation of the function as follows: ##EQU2## Ifthe loudness of the original data is greater than 50 phons, thenrelative weight values for a range of frequencies can be described asfollows:

W 1000 Hz=W 100 Hz×(3.2)

W 500 Hz=W 100 Hz×(2.5)

W 200 Hz=W 100 Hz×(1.8)

W 100 Hz=W 50 Hz×(1.8)

To determine an absolute set of weight values, an arbitrary weight valuemay be chosen for a particular frequency (e.g., W 100 Hz=1) and weightvalues determined for other frequencies either directly from the aboveequations or by extrapolation.

Alternatively, linear weight values may be used. Other weighting schemes(e.g., logarithmic, et cetera) may also be used in accordance with theinvention.

In one embodiment of this invention, the above process is more fullydescribed in FIG. 6. Following FFT 400, a density function is calculatedfor low frequency components of the FFT of from 20-350 HZ. The densityfunction is calculated 401 (source code R1) in accordance with theinvention by integrating the FFT across the frequency range (20-350 HZ)and dividing by the number of frequency components integrated. Likewise,a density function is calculated for mid-frequencies (350-750 HZ) 402(source code R2) and for high frequencies (750-1200 HZ) 403 (source codeR3).

The density functions are then compared 404 (source code R4). If the lowfrequency density function is greater than the mid-frequency, and/or themid-frequency is greater than the high-frequency density, then the eventis determined to be snoring 405. If not, then the event is not a snore407. (Corresponding references HA and 406. HC and 418, H5 and 419, HBand 420, CL13 and 433, and EMULATE UPPP and 435 indicate connectiononly). Another way to detect whether a breathing event is a snore, is touse another microphone not in the air flow path and to use a simpleamplitude threshold criteria to identify snoring.

Following a determination that an event is snoring, a set of thresholdvalues are calculated 408 (source code H1). An FFT sample is then loaded411 (source code H21) and compared with a threshold value 413 (sourcecode H3) to insure that the FFT sample is not outside a range ofinterest.

If the FFT sample is within the range of interest, then a determination414 (source code H4) is made as to where it falls within the spectrum(low, medium, or high) and the FFT sample is then compared 415-417 withan appropriate threshold value L.TH1, MTH1, or H.TH1) to eliminateinsignificant peaks.

If the FFT sample is greater than the relevant threshold, then adetermination is made 421 as to whether the sample is the first FFTsample of a new maxima 423. If it is the first FFT sample of a newmaxima, then the counter harm.reset is reset.

If the FFT sample is not the first FFT sample of a maxima, then acurrent harm.reset value is compared with a threshold value 424 (sourcecode H6) and incremented 427 (source code H8) before being compared asecond time with the threshold value 429 (source code H9). If in eithercomparison 426, 429 it is determined that the value of harm.resetexceeds the threshold (harm.term) the FFT sample is outside the range tobe considered and is not a maxima 427 and the parameters are reset 428(source code H7) for consideration of another maxima. If harm.resetvalue of an FFT sample were less than the value of harm.term, then theFFT sample will be retrieved under pre-existing parameters.

A sample number, "i", is incremented 409 and the incremented samplenumber compared 410 to an event size (1024 samples). If the samplenumber is less than the event size, then another sample from the samemaxima is retrieved 411. If not, the processor 30 proceeds 412 to lookfor fundamentals and harmonics.

The processor first tests if one of the identified frequencies was amaxima 430 (source code C11) indicating the presence of a fundamentalfrequency. If no fundamental is found, the event is rated 431 (sourcecode CL8) as a snore index 5 and the result reported 444 (source codeCL13) to a summary file.

If a maxima is found 430 the processor 30 determines the fundamentalfrequency to be the first maxima frequency 432 (source code CL2). Theprocessor 30 then loads a next maxima 434 (source code CL3) anddetermines 436 (source code CL4) if it is an integer multiple of thefundamental. If so, another maxima is loaded 434 and considered 436. Ifnot, the processor attempts to determine 437, 438 whether the firstmaxima is a multiple of the fundamental.

Follow the identification (grouping) of fundamentals and harmonics 106,the processor emulates the effect of a UPPP. The processor first sets439 (source code CL8) a frequency width equal to the fundamentalfrequency divided by 4. The processor then zeros 440 (source code CL9)all FFT harmonic values closer than the calculated frequency width tothe fundamental and the harmonics frequency. An FFT is then calculated441 (source code CL10) of a simulated UPPP to the original FFT ratios.The ratios are then used to determine 442 (source code CL11) a sourceindex which is then corrected 443 (source code CL12) before saving andprinting 444 (source code CL13) a summary report. A normalized snoreindex for each snore index classification (1-5) may be calculated bysquaring the snore index for each event of each class, summing thesquared indexes of each class, and dividing the summed indexes of eachclass by the summed total of all classes.

The summary may include a list of the dominant fundamental frequenciesas well as secondary fundamentals detected. An estimation of therelative energy in the fundamental (and harmonics) as opposed to allfrequencies, may be provided. The energy content of certain fundamentalfrequencies, compared to appropriate threshold values, may be used as anindicia of snoring from multiple sources or, otherwise, as an indicia ofa velum snore. The relative energy content of fundamentals and harmonicsof from 20-300 Hz and harmonics against total energy of sound may becalculated and included in the summary as a velum snore index.

The summary may identify the soft palate of the subject as the source ofthe snoring and label the snoring as a velum snore when the sound ispredominantly of a fundamental frequency of from 20-150 Hz andharmonics, and a non-velum snore involving the tonsils when thefundamental frequency is above 130 Hz. Where the FFT indicates a diffusesound, the summary may indicate a non-velum snore implicating thepharyngal and nose or otherwise a mixed source when the snoring containsan indicia of multiple sources, such as when the energy of fundamentalfrequencies and harmonics compared to total sound is below a threshold.The summary may also provide an indication of the different types ofevents occurring during a sleep interval by indicating a velum snoreindex from the total velum, non-velum, and mixed snores during a timeinterval.

In another embodiment of the invention other methods are used toidentify the presence of harmonic patterns based in the 20-300 Hz range.One method involves finding a first value "d" and a second value "a"such that during the snoring event the wave amplitude in the time domainwill comply with the equation as follows:

    S(t)=aS(t-d)+δ

where "d" is the cycle time of the fundamental frequency in seconds, "a"is an amplitude that is usually close to 1 that changes very slowly, and"δ" is an allowed error. If "d" is greater than 30 milliseconds, then"d" is the fundamental frequency wave length in seconds. If "d" is muchless than 30 milliseconds, then "d" will be centered around a harmonicwave length and the equation will be valid for only subsections of thesnoring event.

The foregoing specification describes only the preferred embodiments ofthe inventions as shown. Other embodiments besides the ones describedabove may be articulated as well. The terms and expressions, therefore,serve only to describe the invention by example only and not to limitthe invention. It is expected that others will perceive differenceswhich, while differing from the foregoing, do not depart from the spiritand scope of the invention herein described and claimed.

In another embodiment of the invention, data analysis is performed ondata received directly from the subject by a processor 30 located nearthe sleeping subject. Data may also be recorded for later evaluation.

We claim:
 1. A method of detecting and analyzing sleep disorderscomprising the steps of:monitoring sound produced by a sleeping subject;digitizing the monitored sound at a sampling rate sufficiently high andfor periods of sufficient duration to capture apnea and snoringinformation; recording at least a portion of the digitized monitoredsound; identifying at least one of snoring and apnea within the recordedsound; and analyzing the identified snoring and/or apnea to locate upperairway structural sources generating the snoring and/or the apnea. 2.The method as in claim 1 further comprising the step of displayingportions of the recorded sound and listening to selected portionsselected based upon the displayed portions.
 3. The method as in claim 1further including the step of searching the recorded sound for temporalgaps exceeding an apnea threshold and upon detecting such a temporal gapoutputting an indication of apnea.
 4. The method as in claim 1 furtherincluding the step of analyzing the recorded sound using frequencydomain methods.
 5. The method as in claim 1 wherein the digitizing isperformed for multiple periods each of which is substantially less thanthe entire duration that the sleeping subject is asleep.
 6. The methodof claim 2 further comprising the step of allocating a separaterecording channel for each of at least two microphones for continuouslyrecording the monitored sound.
 7. The method as in claim 1 furthercomprising the step of recording physiological data of the sleepingsubject including at least one parameter from the group including bloodoxygen desaturation level, body position, respiration effort, and videoimage of the sleeping subject.
 8. The method as in claim 1 furthercomprising the step of using two separate microphones, one proximate thenose and one proximate the mouth and ruling out the nose or mouth of thesleeping subject as the source of the snoring or breathing when thesound from the respective microphone is negligible.
 9. The method as inclaim 2 wherein the step of recording further comprises the step ofreducing the digital monitored sound and storing the result as a reducedfile and wherein the step of displaying comprises displaying the reducedfile.
 10. The method as in claim 1 wherein the step of monitoring thesound of the sleeping subject further comprises the step of detecting anaudio signal from the sleeping subject through use of at least onecordless microphone.
 11. The method as in claim 1 wherein the step ofanalyzing the monitored sound for respiratory system structural sourcesof snoring further comprises the step of generating a raw data file anda reduced file.
 12. The method as in claim 1 further comprising the stepof automatically identifying a data interval of the recorded sound asbreathing by comparison with an amplitude breathing threshold andtemporal breathing threshold.
 13. The method as in claim 12 furthercomprising the step of automatically identifying a data interval of therecorded sound as snoring by evaluating a frequency content of thespectrum in the data interval and comparison with a sound threshold. 14.The method as in claim 1 further comprising the step of analyzing forstructural sources of snoring while the monitored sound is recordedthrough use of a processor attached to the recorder.
 15. The method ofclaim 4 further comprising the step of using a plurality of temporalsizes of data for frequency analyzing the snoring.
 16. The method ofclaim 11 further comprising the step of tabulating and outputting anumber indicating total velum, non-velum and mixed snore during a timeinterval.
 17. The method of claim 1 further comprising the step ofcomparing the monitored sound with a first recording threshold level anddeactivating the recorder when the monitored sound exceeds the firstthreshold level for at least a first time period and otherwisereactivating recording when the monitored sound does not exceed thethreshold for a second time period.
 18. The method of claim 1 furthercomprising the step of reducing the digitized monitored sound andstoring the result as a reduced data file.
 19. The method of claim 1further comprising the step of reducing the digitized monitored soundand storing the reduced data in conjunction with selected portions ofthe digitized monitored sound.
 20. The method of claim 3 wherein thestep of searching comprises electronically processing the recorded soundto automatically detect temporal gaps exceeding an apnea threshold, andautomatically generating an indication of apnea detection upon detectingsuch a temporal gap.
 21. A method of detecting and analyzing sleepdisorders comprising the steps of:monitoring sound produced by asleeping subject; and identifying snoring within the monitored sound;identifying a soft palate of the sleeping subject as a source of thesnoring when the frequency content of the snoring is substantially afundamental frequency of between a predetermined upper and lowerfrequency threshold and harmonics of the fundamental frequency.
 22. Themethod of claim 21 further comprising identifying non-velum sources ofthe sleeping subject as the source of the snoring when the frequencycontent of the snoring is substantially of a fundamental frequencygreater than an upper threshold frequency and harmonics of thefundamental frequency.
 23. The method of claim 21 further comprisingidentifying one of pharyngal and nose as the source of snoring when afourier transform of the recorded sound indicates a diffuse soundsource.
 24. The method of claim 23 further comprising recording at leasta portion of the monitored sound.
 25. The method of claim 23 furthercomprising identifying apnea events by electronically processing themonitored sound to automatically detect temporal gaps exceeding an apneathreshold.
 26. A method for use in detecting and analyzing sleepdisorders comprising the steps of:monitoring sound produced by asleeping subject including a recording at least a portion of themonitored sound; comparing the monitored sound with a first recordingthreshold level; and deactivating recording when the monitored soundexceeds the first threshold level for at least a first time period. 27.The method of claim 26 further comprising reactivating recording whenthe monitored sound does not exceed the threshold for a second timeperiod.
 28. The method of claim 27 further comprising comparing themonitored sound with a second threshold and reactivating the recorderwhen the monitored sound exceeds the first and second thresholds for athird time period.
 29. The method of claim 26 wherein the step ofmonitoring comprises providing a buffer to continuously buffer a shortsegment of monitored sound.
 30. A method of detecting and analyzingsleep disorders comprising the steps of:monitoring sound produced by asleeping subject; electronically processing the monitored sound toautomatically identify breathing events; electronically processing themonitored sound to automatically detect temporal gaps in the breathingevents to identify apnea events; electronically processing the monitoredsound to automatically identify snoring events.
 31. The method of claim30 further comprising automatically identifying an interval of themonitored sound as breathing by comparison with an amplitude breathingthreshold and a temporal breathing threshold.
 32. The method of claim 30further comprising automatically determining the duration and a relativemaximum for breathing events.
 33. The method of claim 30 furthercomprising generating a reduced data file from raw data of the monitoredsound and using the reduced data file to perform at least a portion ofthe electronic processing.
 34. The method of claim 30 whereinautomatically identifying snoring events further comprises identifyinglocal maxima within the monitored sound.
 35. The method of claim 30further comprising automatically identifying an interval of themonitored sound as snoring by evaluating frequency content of thespectrum of the interval and by comparison with a snoring threshold. 36.The method of claim 34 wherein automatically identifying snoring eventsfurther comprises performing fourier transforms based on the localmaxima.
 37. A method for use in detecting and analyzing sleep disorderscomprising the steps of:monitoring sound produced by a sleeping subject;storing at least a portion of the monitored sound; displaying at leastportions of the stored monitored sound or of data derived from themonitored sound; listening to selected portions of the displayedportions.
 38. The method of claim 37 wherein at least one predeterminedclassification of breathing event is apnea.
 39. The method of claim 37wherein at least one predetermined classification of breathing event issnoring.
 40. The method of claim 38 further comprising listening toselected portions of the displayed portions selected based upon thedisplayed portions to identify snoring events.
 41. The method of claim38 wherein the monitored sound in digitized and reduced to form areduced data file, and the step of displaying comprises displayingportions of the reduced data file.
 42. The method of claim 37 whereinthe step of storing further comprises storing portions of the monitoredsound determined using a sound threshold.
 43. The method of claim 42wherein the sound threshold is at least one of an upper volume thresholdto identify periods of loud sound levels and a low volume threshold toidentify periods of low sound levels.
 44. The method of claim 37 whereinthe step of listening comprises listening to portions selectedautomatically.